PROJECT SUMMARY Atrial fibrillation (AF) is a highly prevalent disease affecting 5.2 million Americans, costs the US $6-26 billion per year, and increases the risk of cardiovascular disease, stroke, and death. Selecting the optimal treatment for each AF patient remains a daily clinical challenge as no single treatment is best in all cases. Symptomatic patients are most frequently treated pharmacologically, or by catheter ablation to isolate or destroy aberrant atrial tissue. However, both are commonly ineffective and there are no consistent predictors of response. Pathological atrial fibrosis is a major contributor to sustaining AF, has repeatedly been implicated in its pathogenesis and is proposed as a biomarker for personalizing treatment. We propose to use cardiac MRI (CMR) mechanics-based measures to identify localized atrial fibrosis. Atrial fibrosis fosters chaotic electrophysiology and also attenuates local atrial mechanics, decreases contractility, and increases stiffness. The impact on atrial mechanics is substantial. Therefore, we hypothesize that attenuated atrial mechanics provide a robust measure of atrial fibrosis. The result of this project will be the first histologically validated, reproducible and repeatable clinical tool that enables estimation of atrial fibrosis burden. The aims of this grant will exploit the mechanistic link between atrial fibrosis and atrial mechanics to develop and validate a clinical workflow for measuring a mechanics-based classifier of fibrosis. The overall objective is to establish a mechanics-based and discriminatory measure of histologically validated atrial fibrosis. The following aims are designed to achieve this objective. AIM 1. To robustly measure 3D atrial CMR strain and stiffness in sinus rhythm and AF. Atrial motion ? even during AF ? is readily apparent on CMR. Our free-breathing and arrhythmia insensitive CMR protocol enables measuring atrial mechanics without the need for contrast or the limitations of echocardiography, nor the radiation of CT. We seek to detect atrial fibrosis by identifying impaired atrial mechanics. AIM 2. Validate and benchmark a CMR mechanics-based classifier of atrial fibrosis. The optimal index for identifying local atrial fibrosis from atrial mechanics is not known. Training and validating a classifier requires a ground truth, which we will measure directly using histology. The classifier will then be benchmarked against conventional markers of atrial fibrosis (voltage mapping and LGE-CMR). Public Health Significance ? Identifying patients with atrial fibrillation (AF) that will respond to specific treatment strategies such as ablation is a daily challenge for cardiologists. Selecting the optimal treatment for each AF patient remains an open challenge. The results of this work will enable clinicians to better manage patients with atrial fibrillation by helping to identify the atrial fibrosis burden using cardiac MRI based methods.